Semantic DMN: Formalizing and Reasoning About Decisions in the Presence of Background Knowledge

TL;DR

This paper introduces a formal framework combining DMN decision models with background domain knowledge using first-order logic, enabling automated reasoning about decisions with background context.

Contribution

It formalizes the integration of DMN decision graphs with background knowledge using a logic-based semantics and reasoning tasks.

Findings

Formal semantics for DMN with background knowledge

Verification tasks are formalized as DL reasoning services

Framework applied successfully in maritime security case study

Abstract

The Decision Model and Notation (DMN) is a recent OMG standard for the elicitation and representation of decision models, and for managing their interconnection with business processes. DMN builds on the notion of decision tables, and their combination into more complex decision requirements graphs (DRGs), which bridge between business process models and decision logic models. DRGs may rely on additional, external business knowledge models, whose functioning is not part of the standard. In this work, we consider one of the most important types of business knowledge, namely background knowledge that conceptually accounts for the structural aspects of the domain of interest, and propose decision knowledge bases (DKBs), which semantically combine DRGs modeled in DMN, and domain knowledge captured by means of first-order logic with datatypes. We provide a logic-based semantics for such an integration, and formalize different DMN reasoning tasks for DKBs. We then consider background knowledge formulated as a description logic ontology with datatypes, and show how the main verification tasks for DMN in this enriched setting can be formalized as standard DL reasoning services, and actually carried out in ExpTime. We discuss the effectiveness of our framework on a case study in maritime security.